



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor

class  snake_case__(unittest.TestCase     ):

					"""simple docstring"""






					def __init__(				self   :      Optional[Any] ,  SCREAMING_SNAKE_CASE   :      Dict ,  SCREAMING_SNAKE_CASE   :      Tuple=7 ,  SCREAMING_SNAKE_CASE   :      Tuple=3 ,  SCREAMING_SNAKE_CASE   :      str=30 ,  SCREAMING_SNAKE_CASE   :      List[str]=400 ,  SCREAMING_SNAKE_CASE   :      List[str]=True ,  SCREAMING_SNAKE_CASE   :      List[Any]=None ,  SCREAMING_SNAKE_CASE   :      Optional[int]=0.9 ,  SCREAMING_SNAKE_CASE   :      Optional[Any]=None ,  SCREAMING_SNAKE_CASE   :      Dict=True ,  SCREAMING_SNAKE_CASE   :      List[str]=[0.5, 0.5, 0.5] ,  SCREAMING_SNAKE_CASE   :      List[Any]=[0.5, 0.5, 0.5] ,  ):
									lowercase__	:       Optional[Any]						=	size if size is not None else {"""shortest_edge""": 30}
									lowercase__	:       List[Any]						=	crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
									lowercase__	:       int						=	parent
									lowercase__	:       List[Any]						=	batch_size
									lowercase__	:       Union[str, Any]						=	num_channels
									lowercase__	:       str						=	min_resolution
									lowercase__	:       str						=	max_resolution
									lowercase__	:       Optional[Any]						=	do_resize_and_center_crop
									lowercase__	:       Dict						=	size
									lowercase__	:       List[Any]						=	crop_pct
									lowercase__	:       Optional[int]						=	crop_size
									lowercase__	:       str						=	do_normalize
									lowercase__	:       Tuple						=	image_mean
									lowercase__	:       str						=	image_std







					def 					snake_case	(				self   :      Union[str, Any]	):
									return {
									    "size": self.size,
									    "do_resize_and_center_crop": self.do_resize_and_center_crop,
									    "crop_pct": self.crop_pct,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}






@require_torch
@require_vision
class  snake_case__(_snake_case							,     unittest.TestCase     ):

					"""simple docstring"""






					lowercase_								=						PoolFormerImageProcessor if is_vision_available() else None







					def 					snake_case	(				self   :      Any	):
									lowercase__	:       List[str]						=	PoolFormerImageProcessingTester(self	)







					@property
					def 					snake_case	(				self   :      int	):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 					snake_case	(				self   :      List[str]	):
									lowercase__	:       Optional[Any]						=	self.image_processing_class(**self.image_processor_dict	)
									self.assertTrue(hasattr(__snake_case ,  "do_resize_and_center_crop"	)	)
									self.assertTrue(hasattr(__snake_case ,  "size"	)	)
									self.assertTrue(hasattr(__snake_case ,  "crop_pct"	)	)
									self.assertTrue(hasattr(__snake_case ,  "do_normalize"	)	)
									self.assertTrue(hasattr(__snake_case ,  "image_mean"	)	)
									self.assertTrue(hasattr(__snake_case ,  "image_std"	)	)







					def 					snake_case	(				self   :      Any	):
									lowercase__	:       Dict						=	self.image_processing_class.from_dict(self.image_processor_dict	)
									self.assertEqual(image_processor.size ,  {"shortest_edge": 30}	)
									self.assertEqual(image_processor.crop_size ,  {"height": 30, "width": 30}	)

									lowercase__	:       Dict						=	self.image_processing_class.from_dict(self.image_processor_dict ,  size=42 ,  crop_size=84	)
									self.assertEqual(image_processor.size ,  {"shortest_edge": 42}	)
									self.assertEqual(image_processor.crop_size ,  {"height": 84, "width": 84}	)







					def 					snake_case	(				self   :      Tuple	):
									pass







					def 					snake_case	(				self   :      List[Any]	):
									# Initialize image_processing
									lowercase__	:       List[str]						=	self.image_processing_class(**self.image_processor_dict	)
									# create random PIL images
									lowercase__	:       str						=	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=__snake_case	)
									for image in image_inputs:
													self.assertIsInstance(__snake_case ,  Image.Image	)

									# Test not batched input
									lowercase__	:       int						=	image_processing(image_inputs[0] ,  return_tensors="pt"	).pixel_values

									self.assertEqual(
									    encoded_images.shape ,  (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    ) ,  )

									# Test batched
									lowercase__	:       Optional[int]						=	image_processing(__snake_case ,  return_tensors="pt"	).pixel_values
									self.assertEqual(
									    encoded_images.shape ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    ) ,  )







					def 					snake_case	(				self   :      Optional[int]	):
									# Initialize image_processing
									lowercase__	:       Optional[Any]						=	self.image_processing_class(**self.image_processor_dict	)
									# create random numpy tensors
									lowercase__	:       List[Any]						=	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=__snake_case ,  numpify=__snake_case	)
									for image in image_inputs:
													self.assertIsInstance(__snake_case ,  np.ndarray	)

									# Test not batched input
									lowercase__	:       int						=	image_processing(image_inputs[0] ,  return_tensors="pt"	).pixel_values
									self.assertEqual(
									    encoded_images.shape ,  (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    ) ,  )

									# Test batched
									lowercase__	:       Union[str, Any]						=	image_processing(__snake_case ,  return_tensors="pt"	).pixel_values
									self.assertEqual(
									    encoded_images.shape ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    ) ,  )







					def 					snake_case	(				self   :      Any	):
									# Initialize image_processing
									lowercase__	:       Optional[Any]						=	self.image_processing_class(**self.image_processor_dict	)
									# create random PyTorch tensors
									lowercase__	:       Dict						=	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=__snake_case ,  torchify=__snake_case	)
									for image in image_inputs:
													self.assertIsInstance(__snake_case ,  torch.Tensor	)

									# Test not batched input
									lowercase__	:       List[Any]						=	image_processing(image_inputs[0] ,  return_tensors="pt"	).pixel_values
									self.assertEqual(
									    encoded_images.shape ,  (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    ) ,  )

									# Test batched
									lowercase__	:       List[str]						=	image_processing(__snake_case ,  return_tensors="pt"	).pixel_values
									self.assertEqual(
									    encoded_images.shape ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    ) ,  )








'''simple docstring'''


import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


UpperCAmelCase_						:  List[Any]				  =						'sshleifer/bart-tiny-random'
UpperCAmelCase_						:  Tuple				  =						'patrickvonplaten/t5-tiny-random'





@require_torch
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							@cached_property
							def UpperCAmelCase_      (       self    ):
											return AutoConfig.from_pretrained(__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			int							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=1    )
											self.assertEqual(student.config.num_hidden_layers       ,   1    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			int							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			str							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=__snake_case    )
											self.assertEqual(student.config.encoder_layers       ,   1    )
											self.assertEqual(student.config.decoder_layers       ,   self.teacher_config.encoder_layers    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=1    )
											self.assertEqual(student.config.encoder_layers       ,   1    )
											self.assertEqual(student.config.decoder_layers       ,   1    )






							def UpperCAmelCase_      (       self    ):
											with self.assertRaises(__snake_case    ):
															create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=__snake_case       ,   d=__snake_case    )




